Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant K d values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets.

Metallo-β-lactamases (MBLs), such as New Delhi metallo-β-lactamase (NDM-1) have spread world-wide and present a serious threat. Expression of MBLs confers resistance in Gram-negative bacteria to all classes of β-lactam antibiotics, with the exception of monobactams, which are intrinsically stable to MBLs. However, existing first generation monobactam drugs like aztreonam have limited clinical utility against MBL-expressing strains because they are impacted by serine β-lactamases (SBLs), which are often co-expressed in clinical isolates. Here, we optimized novel monobactams for stability against SBLs, which led to the identification of LYS228 (compound 31). LYS228 is potent in the presence of all classes of β-lactamases and shows potent activity against carbapenem-resistant isolates of Enterobacteriaceae (CRE).

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

The Food and Drug Administration Adverse Event Reporting System (FAERS) is the primary source for post-marketing pharmacovigilance. Though potentially highly useful, the database reflects reporting biases, stimulated reporting, and suffers from lack of standardization and the use of multiple drug synonyms. These biases can suggest adverse drug reactions (ADRs) where none exist, and can obscure others that do exist. To decrease the noise in FAERS, and to reinforce important associations, we mapped over 750,000 drug identifiers in FAERS to the normalized chemical structures of their ingredients. This illuminated associations that would not otherwise be apparent, and also allowed a time-resolved analysis of ADR reporting. It also revealed similarities between drugs and adverse events across therapeutic classes, enabling unbiased classification of adverse events, indications, and drugs with similar clinical profiles. For instance, comparison of two selective cyclooxygenase-2 inhibitors, celecoxib and rofecoxib finds distinctive FAERS profiles after time-resolved analysis. We also investigated key idiosyncrasies, such as confusion between drug indications and drug ADRs, which can tar a drug treating a life-threatening disease, like thalidomide’s use against myeloma, with a deadly ADR that is likely the result of the disease itself, multiplications of the same report, which unjustifiably increases its apparent importance, and the correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole and risperidone, and of kinase drugs targeting the VEGF receptor (VEGF-R2), demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlaying mechanisms.

Over the last decade, a number of modern in vivo and in vitro methodologies and in silico tools have been developed to investigate toxicokinetic and toxicodynamic processes of chemicals, enabling an understanding of mechanism of action at different levels of biological organization from the whole organism to organ, cellular, and molecular levels. These methodologies provide the opportunity to move toward a mechanistic understanding of toxicity, to reduce animal use in toxicologic research, and to integrate toxicology very early in the drug discovery process enabling deselection of those scaffolds and molecules carrying a high risk of failure due to adverse drug reactions.

Secondary pharmacology is an essential component of drug discovery and is used extensively in the pharmaceutical industry for achieving optimal specificity of new drugs via early hazard identification and off-target mitigation. The importance of this discipline has been achieved by increasing its translational value, based on the recognition of biological target-drug molecule-adverse drug reaction (ADR) associations and integration of secondary pharmacology data with pharmacokinetic parameters. Information obtained from clinical ADRs, from recognition of specific phenotypes of animal models and from hereditary diseases provides increasing regulatory confidence in the target-based approach to ADR prediction and mitigation. Here, we review the progress of secondary pharmacology during the past decade and highlight and demonstrate its applications and impact in drug discovery.

Blockade of the hERG potassium channel prolongs the ventricular action potential (AP) and QT interval, and triggers early after depolarizations (EADs) and torsade de pointes (TdP) arrhythmia. Opinions differ as to the causal relationship between hERG blockade and TdP, the relative weighting of other contributing factors, definitive metrics of preclinical proarrhythmicity, and the true safety margin in humans. Here, we have used in silico techniques to characterize the effects of channel gating and binding kinetics on hERG occupancy, and of blockade on the human ventricular AP. Gating effects differ for compounds that are sterically compatible with closed channels (becoming trapped in deactivated channels) versus those that are incompatible with the closed/closing state, and expelled during deactivation. Occupancies of trappable blockers build to equilibrium levels, whereas those of non-trappable blockers build and decay during each AP cycle. Occupancies of ~83% (non-trappable) versus ~63% (trappable) of open/inactive channels caused EADs in our AP simulations. Overall, we conclude that hERG occupancy at therapeutic exposure levels may be tolerated for nontrappable, but not trappable blockers capable of building to the proarrhythmic occupancy level. Furthermore, the widely used Redfern safety index may be biased toward trappable blockers, overestimating the exposure-IC50 separation in nontrappable cases.

FAERS is a postmarketing reporting system of adverse drug reactions (ADRs), developed by the Food and Drug Administration and released for open access for the public. FAERS is particularly important to track side effects that have not been observed during clinical trials because of the limited number of patients, less diverse patient populations than in post-registration phase, or drug–drug interactions not investigated during the clinical trials. While it is an invaluable source for postmarketing pharmacovigilance, it has several shortcomings that if not recognized could lead to misinterpretation of stored information about diseases, medicines, and their side effects.While in this chapter we highlight several biases of FEARS, we also give examples how it supports early, target-based safety assessment by providing valuable information on molecule–target–ADR associations.

Suicide is a major public health concern with emerging understanding of its neuropharmacological basis. Treatment-related suicidal ideation and behavior (SIB) as adverse events of psychiatric and nonpsychiatric drugs are under increasing public, legal, and regulatory scrutiny. Prospective assessment of SIB is emerging as a challenging safety requirement by health authorities worldwide for the development of certain drugs while the underlying risk factors remain ill defined. To help with the understanding and harmonization of risk factors that trigger a prospective assessment of SIB in clinical trials, Bristol-Myers Squibb, Eli Lilly, Novartis, Pfizer, and Roche/Genentech present a consensus framework for risk assessment and decision making of SIB during drug development. Application of this strategy is based on chemical and pharmacological similarities of compounds with clinical evidence of suicidal intent, target/indication classes associated with high incidence of SIB, in vitro neuropharmacological activity profile, in vivo ADME properties, the patient population of the underlying indication, and regulatory precedents.

The existence of different binding sites for angiotensin II (Ang II) was postulated for years, but up to the early 1980s, the tools were not available to demonstrate it. Peptidic and nonpeptidic structures were subsequently developed and these helped three independent groups to arrive at the discovery of the two main angiotensin receptors at about the same time. There was some confusion regarding the nomenclature until the AHA Council for High Blood Pressure Research established a nomenclature committee in 1990, which proposed the names AT 1 and AT 2 . The selective AT 2 receptor ligands CGP 42112 and PD 123177 and the AT 1 receptor ligand DuP 753 were the first tools to study these receptors. It rapidly became apparent that a balance exists between the two receptors, which seem to be mutually antagonistic. Although the role of the AT 1 receptor is well understood, there is still a lot of ambiguity regarding the AT 2 receptor. The peptidic nature of CGP 42112 limits its use and the development of a nonpeptidic AT 2 agonist like C21 is a clear requirement to understand the pathophysiological role of this receptor and its clinical interest.

Matched molecular pair analysis (MMPA) has become a major tool for analyzing large chemistry data sets for promising chemical transformations. However, the dependence of MMPA predictions on data constraints such as the number of pairs involved, experimental uncertainty, source of the experiments, and variability of the true physical effect has not yet been described. In this contribution the statistical basics for judging MMPA are analyzed. We illustrate the connection between overall MMPA statistics and individual pairs with a detailed comparison of average CHEMBL hERG MMPA results versus pairs with extreme transformation effects. Comparing the CHEMBL results to Novartis data, we find that significant transformation effects agree very well if the experimental uncertainty is considered. This indicates that caution must be exercised for predictions from insignificant MMPAs, yet highlights the robustness of statistically validated MMPA and shows that MMPA on public databases can yield results that are very useful for medicinal chemistry.

Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 μM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.